3 citations · 3 across the 4 of their papers we have counts for
4 papers
High-dimensional Bayesian filtering through deep density approximation
Kasper Bågmark, Filip Rydin
In this work, we systematically benchmark two recently developed deep density methods for nonlinear filtering. We model the filtering density of a discretely observed stochastic di…
Nonlinear filtering based on density approximation and deep BSDE prediction
Kasper Bågmark, Adam Andersson, Stig Larsson
A novel approximate Bayesian filter based on backward stochastic differential equations is introduced. It uses a nonlinear Feynman--Kac representation of the filtering problem and…
A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
Kasper Bågmark, Adam Andersson, Stig Larsson +1
A numerical scheme for approximating the nonlinear filtering density is introduced and its convergence rate is established, theoretically under a parabolic Hörmander condition, and…
An energy-based deep splitting method for the nonlinear filtering problem
Kasper Bågmark, Adam Andersson, Stig Larsson
The purpose of this paper is to explore the use of deep learning for the solution of the nonlinear filtering problem. This is achieved by solving the Zakai equation by a deep split…